Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack (ELK), it centrally stores your data so you can discover the expected and uncover the unexpected. In this post, we’re investigating some features and out of the box use cases for ElasticSearch in the field of NLP. Search Enhancement Features ElasticSearch provides us with a sort of cool stuff to enhance our end-user search experience. You Complete Me Effective search is not just about returning relevant results when a user types in a search phrase, … Continue reading ElasticSearch Out of the Box Use Cases
So, you’ve decided you want to train your own machine learning model to satisfy your business needs… Great!But what are your options to train the model? Actually, you need to decide: Whether you want to train your model on your … Continue reading Machine Learning Training Options
In a previous post, we talked in detail about Test Driven Development (TDD) its main methodology, benefits, pitfalls, and best practices. According to the major differences between ML-based code and traditional programming, in this post, we’re discussing the applicability of … Continue reading Test Driven Machine Learning
Many sites on the internet allow their users to specify tags for their content. The most famous example of such sites is Tumblr where each post on this social network can hold a manually selected set of tags. These tags … Continue reading Auto-Tagging Content with NLP
Clickbait is a type of hyperlink on a web page that has catchy or provocative headlines difficult for most users to resist, they tell you exactly what you’re about to see, with just enough of a tease at the end … Continue reading How to Detect Clickbait Headlines using NLP?
In this post, we are exploring how Google’s AutoML can help us in Almeta in developing automatic Arabic language processing tools. Before start if you are not familiar with the term AutoML you can refer to our previous post on this topic. Who is Google AutoML for? and When to Use It? The targeted audience by Google’s cloud autoML are people who have limited knowledge in machine learning. The main goal of this cloud service is to let the user build his own AI model that is tailored to his business needs, if the provided services by Google’s AI API … Continue reading Google’s AutoML Overview
When applying machine learning models, we’d usually do data pre-processing, feature engineering, feature extraction and, feature selection. After this, we’d select the best algorithm and tune our parameters in order to obtain the best results. AutoML is a series of … Continue reading Automated Machine Learning (AutoML)